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Thibodeau, Asa; Eroglu, Alper; McGinnis, Christopher S; Lawlor, Nathan; Nehar-Belaid, Djamel; Kursawe, Romy; Marches, Radu; Conrad, Daniel N; Kuchel, George A; Gartner, Zev J; Banchereau, Jacques; Stitzel, Michael L; Cicek, A Ercument; Ucar, Duygu
Genome Biology, 09/2021, Letnik: 22, Številka: 1Journal Article
Detecting multiplets in single nucleus (sn)ATAC-seq data is challenging due to data sparsity and limited dynamic range. AMULET (ATAC-seq MULtiplet Estimation Tool) enumerates regions with greater than two uniquely aligned reads across the genome to effectively detect multiplets. We evaluate the method by generating snATAC-seq data in the human blood and pancreatic islet samples. AMULET has high precision, estimated via donor-based multiplexing, and high recall, estimated via simulated multiplets, compared to alternatives and identifies multiplets most effectively when a certain read depth of 25K median valid reads per nucleus is achieved.
